In the field of radio communication systems, it is a well-known problem that the power amplifiers present in transmission equipment operate in a non-linear fashion when the power amplifiers are operated near their peak output. As a result, the power amplifier introduces significant signal distortion that can appear in various forms. For example, if more than one signal is input into the power amplifier or power amplification stage, its non-linear characteristics can cause an undesirable multiplicative interaction of the signals being amplified, and the power amplifier's output can contain intermodulation products. These intermodulation products cause interference and crosstalk over the power amplifier's operational frequency range.
In power amplifier design, there is a trade off between distortion performance and efficiency. Linear amplifiers that operate under “Class A” conditions create little distortion but are inefficient, whereas nonlinear amplifiers operated under “Class C” conditions are reasonably efficient but introduce significant distortions. While both efficiency and distortion are important considerations in amplifier design, efficiency becomes increasingly important at high power levels. Because of their efficiency, nonlinear amplifiers are largely preferred, leaving the user with the problem of distortion.
In order to employ nonlinear power amplifiers, techniques have been used to improve linearity and thereby reduce the effects of interference and crosstalk. Linearity can be achieved by application of various linearization techniques that reduce the distortion caused by nonlinear amplification. Conventional amplifier linearization techniques can be broadly categorized as feedback, feedforward, or predistortion.
The last mentioned technique, predistortion, intentionally distorts the signal before the power amplifier so that the non-linearity of the power amplifier can be compensated. According to this technique, linearization is achieved by distorting an input signal according to a predistortion function in a manner that is inverse to the amplifier characteristic function. The predistortion technique can be applied at radio frequency (RF), intermediate frequency (IF), or at baseband.
In the baseband domain, the input signal information is at a much lower frequency, allowing digital methods to be employed. The predistortion function is applied to the input signal with the resulting predistorted signal being upconverted to IF and then finally to the RF carrier frequency. It is also possible to apply adaptive predistortion techniques where feedback from the output of the amplifier is used to update and correct the predistortion function.
The form of the predistortion function is dependent upon the model used to characterize the output of the amplifier. Predistortion functions in the baseband domain are typically implemented as a table of gain and phase weighting values within a digital signal processor. A Cartesian feedback method employs a quadrature representation of the signal being amplified. The incoming quadrature signals I and Q are compared to the feedback quadrature signals. Thus, there are two sets of coefficients, one for each quadrature channel, that are being updated to model the predistortion characteristics. In this manner, gain and phase non-linearities within the amplifier can be compensated. Performance is dependent on the size of the look up table and the number of bits used to represent the signal. Better performance and more adaptivity is achieved with larger look up tables and more bits albeit at the expense of longer processing times.
Predistortion functions are also modeled as polynomials. Ideal amplifiers have linear characteristics; consequently, amplifiers with slight non-linearities can be modeled as polynomials of only a few terms, with only odd terms being employed. Even terms are discarded because their use with negative-valued inputs can interfere with linearity. While processing demands are eased by excluding and limiting the number of terms in the polynomial modeling, performance is sacrificed.
Adaptive methods generally process and model current amplifier characteristics. The current output signal of the amplifier is contrasted against the current input signal to the amplifier. Past inputs are not considered. However, amplifier characteristics are dependent upon frequency due to the speed in which input signals change amplitude as a function of frequency. Exclusion of past inputs precludes modeling those changes and limits the accuracy with which the amplifier can be modeled and thereby limits the bandwidth.
Accordingly, there is a need for a device to more quickly and efficiently determine the characteristics of a frequency dependent amplifier.